• Journal of Intelligence and Information Systems
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• v.20 no.4
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• pp.121-139
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• 2014

Predicting corporate failure has been an important topic in accounting and finance. The costs associated with bankruptcy are high, so the accuracy of bankruptcy prediction is greatly important for financial institutions. Lots of researchers have dealt with the topic associated with bankruptcy prediction in the past three decades. The current research attempts to use ensemble models for improving the performance of bankruptcy prediction. Ensemble classification is to combine individually trained classifiers in order to gain more accurate prediction than individual models. Ensemble techniques are shown to be very useful for improving the generalization ability of the classifier. Bagging is the most commonly used methods for constructing ensemble classifiers. In bagging, the different training data subsets are randomly drawn with replacement from the original training dataset. Base classifiers are trained on the different bootstrap samples. Instance selection is to select critical instances while deleting and removing irrelevant and harmful instances from the original set. Instance selection and bagging are quite well known in data mining. However, few studies have dealt with the integration of instance selection and bagging. This study proposes an improved bagging ensemble based on instance selection using genetic algorithms (GA) for improving the performance of SVM. GA is an efficient optimization procedure based on the theory of natural selection and evolution. GA uses the idea of survival of the fittest by progressively accepting better solutions to the problems. GA searches by maintaining a population of solutions from which better solutions are created rather than making incremental changes to a single solution to the problem. The initial solution population is generated randomly and evolves into the next generation by genetic operators such as selection, crossover and mutation. The solutions coded by strings are evaluated by the fitness function. The proposed model consists of two phases: GA based Instance Selection and Instance based Bagging. In the first phase, GA is used to select optimal instance subset that is used as input data of bagging model. In this study, the chromosome is encoded as a form of binary string for the instance subset. In this phase, the population size was set to 100 while maximum number of generations was set to 150. We set the crossover rate and mutation rate to 0.7 and 0.1 respectively. We used the prediction accuracy of model as the fitness function of GA. SVM model is trained on training data set using the selected instance subset. The prediction accuracy of SVM model over test data set is used as fitness value in order to avoid overfitting. In the second phase, we used the optimal instance subset selected in the first phase as input data of bagging model. We used SVM model as base classifier for bagging ensemble. The majority voting scheme was used as a combining method in this study. This study applies the proposed model to the bankruptcy prediction problem using a real data set from Korean companies. The research data used in this study contains 1832 externally non-audited firms which filed for bankruptcy (916 cases) and non-bankruptcy (916 cases). Financial ratios categorized as stability, profitability, growth, activity and cash flow were investigated through literature review and basic statistical methods and we selected 8 financial ratios as the final input variables. We separated the whole data into three subsets as training, test and validation data set. In this study, we compared the proposed model with several comparative models including the simple individual SVM model, the simple bagging model and the instance selection based SVM model. The McNemar tests were used to examine whether the proposed model significantly outperforms the other models. The experimental results show that the proposed model outperforms the other models.

• Journal of KIISE:Software and Applications
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• v.32 no.4
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• pp.247-257
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• 2005

Generally, the network topology design problem is characterized as a kind of NP-hard combinatorial optimization problem, which is difficult to solve with the classical method because it has exponentially increasing complexity with the augmented network size. In this paper, we propose the efficient approach with two phase that is comprised of evolutionary computation approach based on Prufer number(PN), which can efficiently represent the spanning tree, and a heuristic method considering 2-connectivity, to solve the highly reliable network topology design problem minimizing the construction cost subject to network reliability: firstly, to find the spanning tree, genetic algorithm that is the most widely known type of evolutionary computation approach, is used; secondly, a heuristic method is employed, in order to search the optimal network topology based on the spanning tree obtained in the first Phase, considering 2-connectivity. Lastly, the performance of our approach is provided from the results of numerical examples.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.43 no.9
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• pp.787-798
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• 2015

The emergence of mid-to-small satellites has created a need for rapid development with a relatively low cost. However, the development of mid-to-small satellites requires considerable time and cost in early phase, in particular, during the development of mission and system requirements through iterations of conceptual design and mission design. In this research, Spacecraft Conceptual Design Tool(SCDT) which is based on Graphical User Interface(GUI) was developed to reduce the time and cost for early phase development. Furthermore, GUI-based software can make the input values to be editable easily and show users design results in various way. In this paper, the development results of MATLAB GUI-based SCDT are introduced.

• Journal of Electrical Engineering and Technology
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• v.11 no.6
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• pp.1590-1599
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• 2016

The installation of distributed generation (DG) in the electrical networks has numerous advantages. However, connecting and disconnecting of DGs (CADD) leads to some problems in coordination of protection devices due to the changes in the short circuit levels in the different points of network. In this paper, an adaptive method is proposed based on available setting groups (SG) of relays. Since the number of available SG is less than possible CADD states, a classifying index (CI) is defined to categorize the several states in restricted setting groups. Genetic algorithm (GA) with a suitable objective function (OF) is used as an optimization method for the classification. After grouping, a modified coordination method is applied to achieve optimal coordination for each group. The efficiency of the proposed technique is demonstrated by simulation results.

• Journal of Mechanical Science and Technology
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• v.14 no.11
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• pp.1206-1215
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• 2000

This paper introduces the application of evolutionary fuzzy modeling (EFM) in constructing global function approximations to subsequent use in non-gradient based optimizations strategies. The fuzzy logic is employed for express the relationship between input training pattern in form of linguistic fuzzy rules. EFM is used to determine the optimal values of membership function parameters by adapting fuzzy rules available. In the study, genetic algorithms (GA's) treat a set of membership function parameters as design variables and evolve them until the mean square error between defuzzified outputs and actual target values are minimized. We also discuss the enhanced accuracy of function approximations, comparing with traditional response surface methods by using polynomial interpolation and back propagation neural networks in its ability to handle the typical benchmark problems.

• Journal of the Society of Naval Architects of Korea
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• v.43 no.2 s.146
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• pp.156-163
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• 2006

The accuracy of estimates of hydrodynamic coefficients can be increased by using sensitivity-maximizing inputs. In this study, sensitivity-maximizing actuator commands of a submersible, which are sequences of bang-bang type commands, were obtained using Genetic Algorithm (GA) optimization technique. By comparing the total sensitivity values, deduced actuator inputs were found to be superior to the other sea trials. Based on the sensitivity distribution of conventional sea trials and sensitivity distribution results through deduced input scenario a review of submersibles' manoeuvring equations of motion was conducted .

• Proceedings of the Korean Geotechical Society Conference
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• 2002.10a
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• pp.65-84
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• 2002

The Observational Method proposed by Terzaghi can be applied for the safe and economic construction projects where the exact prediction of the behavior of the structures is difficult as in the underground excavation. The method consists of measuring lateral displacement, ground settlement and axial force of supports in the earlier stage of the construction and back analysis technique to find the best fit design parameters such as earth pressure coefficient, subgrade reaction etc, which will minimize the gap between calculated displacement and measured displacement. With the results, more reliable prediction of the later stage can be obtained. In this study, back analysis programs using the Direct Method, based on the Hill Climbing Method were made and evaluated, and to overcome the limits of the method, Genetic Algorithm(GA) was applied and tested for the actual construction cases.

• ETRI Journal
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• v.26 no.1
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• pp.1-6
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• 2004

The photonic quantum ring (PQR) laser is a three dimensional whispering gallery (WG) mode laser and has anomalous quantum wire properties, such as microampere to nanoampere range threshold currents and ${\sqrt{T}}$-dependent thermal red shifts. We observed uniform bottom emissions from a 1-kb smart pixel chip of a $32{\times}32$ InGaAs PQR laser array flip-chip bonded to a 0.35 ${\mu}m$ CMOS-based PQR laser driver. The PQR-CMOS smart pixel array, now operating at 30 MHz, will be improved to the GHz frequency range through device and circuit optimization.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.11
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• pp.4726-4731
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• 2011

This paper proposed the algorithm to select optimal grinding condition for plunge grinding in the batch production unit. In order to apply to the proposed algorithm, the state variable for plunge grinding process was defined and the optimal grinding condition for each cycle in batch production was decided by genetic algorithm. Based on the optimized grinding condition in each cycle, the optimal grinding condition for whole batch production was selected by dynamic programming. The proposed algorithm was evaluated by computer simulation.

• Structural Engineering and Mechanics
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• v.25 no.6
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• pp.637-652
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• 2007

This paper presents stacking sequence optimization of laminated angle-ply cylindrical panel based on natural frequency. Finite element method (FEM) is used to obtain the vibration characteristic of an anisotropic panel using the first order shear deformation theory(FSDT) and genetic algorithm (GA) is used to obtain the optimal stacking sequence of the layers. Cylindrical panel has finite length and arbitrary boundary conditions. The thicknesses of the layers are assumed constant and their angles are specified as design variables. The effect of the number of plies and boundary conditions in the fitness function is considered. Numerical examples are presented for four, six and eight layered anisotropic cylindrical panels.